Human gene expression

Parameters

Document rendering

Analysis input/output

input_folder <- "raw_input" # Where all the large input files are. Ignored by git. 
output_folder <- "results" # Where plots will be saved
output_format <- "pdf" # The file format of saved plots
pub_fig_folder <- "publication"
revision_n <- 1
result_path <- function(name) {
  file.path(output_folder, paste0(name, ".", output_format))
}
save_publication_fig <- function(name, figure_number) {
  file.path(result_path(name), paste0("revision_", revision_n), paste0("figure_", figure_number, "--", name, ".", output_format))
}

Analysis settings

min_p_value <- 0.05

Introduction

Dependencies

This analysis requires Bioconductor packages to work. These packages are not available on CRAN and must be installed using the following code. The code below is not run during the compilation of this page, but is provided so others can quickly find the right software to replicate this analysis.

source("https://bioconductor.org/biocLite.R")
source("http://bioconductor.org/workflows.R")

workflowInstall("rnaseqGene")
biocLite("GO.db")
biocLite("org.Hs.eg.db")
biocLite("airway")
biocLite("DESeq2")

Read in data

Starting from SummarizedExperiment

http://www.bioconductor.org/help/workflows/rnaseqGene/

library(rnaseqGene)
library("airway")
data("airway")
se <- airway

se$dex <- relevel(se$dex, "untrt")
se$dex

## [1] untrt trt   untrt trt   untrt trt   untrt trt  
## Levels: untrt trt

library("DESeq2")
dds <- DESeqDataSet(se, design = ~ cell + dex)

Pre-filtering the dataset

nrow(dds)

## [1] 64102

dds <- dds[ rowSums(counts(dds)) > 1, ]
nrow(dds)

## [1] 29391

The rlog transformation

rld <- rlog(dds, blind=FALSE)
head(assay(rld), 3)

##                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003   9.385683   9.052608   9.516875   9.285338   9.839085
## ENSG00000000419   8.869616   9.138271   9.036116   9.075295   8.972126
## ENSG00000000457   7.961369   7.881385   7.824079   7.921490   7.751699
##                 SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003   9.530311   9.663255   9.277699
## ENSG00000000419   9.131824   8.861534   9.060905
## ENSG00000000457   7.886441   7.957121   7.912123

Differential expression analysis

dds <- estimateSizeFactors(dds)
dds <- DESeq(dds)
(res <- results(dds))

## log2 fold change (MAP): dex trt vs untrt 
## Wald test p-value: dex trt vs untrt 
## DataFrame with 29391 rows and 6 columns
##                    baseMean log2FoldChange      lfcSE       stat      pvalue
##                   <numeric>      <numeric>  <numeric>  <numeric>   <numeric>
## ENSG00000000003 708.6021697    -0.37415193 0.09884432 -3.7852648 0.000153545
## ENSG00000000419 520.2979006     0.20206144 0.10974240  1.8412340 0.065587276
## ENSG00000000457 237.1630368     0.03616620 0.13834538  0.2614196 0.793768939
## ENSG00000000460  57.9326331    -0.08446385 0.24990676 -0.3379815 0.735377161
## ENSG00000000938   0.3180984    -0.08413904 0.15133427 -0.5559814 0.578223585
## ...                     ...            ...        ...        ...         ...
## ENSG00000273485   1.2864477     0.03398815  0.2932360  0.1159071   0.9077261
## ENSG00000273486  15.4525365    -0.09560732  0.3410333 -0.2803460   0.7792120
## ENSG00000273487   8.1632350     0.55007412  0.3725061  1.4766847   0.1397602
## ENSG00000273488   8.5844790     0.10515293  0.3683834  0.2854442   0.7753038
## ENSG00000273489   0.2758994     0.06947900  0.1512520  0.4593591   0.6459763
##                       padj
##                  <numeric>
## ENSG00000000003 0.00128686
## ENSG00000000419 0.19676183
## ENSG00000000457 0.91372953
## ENSG00000000460 0.88385059
## ENSG00000000938         NA
## ...                    ...
## ENSG00000273485         NA
## ENSG00000273486  0.9062268
## ENSG00000273487  0.3389275
## ENSG00000273488  0.9039857
## ENSG00000273489         NA

Converting ENSEMBL IDs to GO IDs

library(GO.db)
library(org.Hs.eg.db)
res$go_id <- mapIds(org.Hs.eg.db,
                    keys=rownames(res),
                    column="GO",
                    keytype="ENSEMBL",
                    multiVals="first")

Fitlering results

Remove genes with no GO annotation

nrow(res)

## [1] 29391

res <- res[!is.na(res$go_id), ]
res <- res[res$go_id %in% keys(org.Hs.eg.db, keytype = "GO"), ]
nrow(res)

## [1] 15351

Remove insignificant genes

nrow(res)
res <- res[(! is.na(res$padj)) & res$padj <= 0.05, ]
nrow(res)

Remove genes with small changes

nrow(res)
res <-  res[abs(res$log2FoldChange) >= 0.5, ]
nrow(res)

Getting classificaiton

term_class <- function(x, current = x, all_paths = TRUE, type = GOCCPARENTS, verbose = TRUE, 
                       valid_relationships = c("is_a")) {
  # Get immediate children of current taxon
  parents = tryCatch({
    possible_parents <- as.list(type[x[1]])[[1]]
    if (! is.null(valid_relationships)) {
      possible_parents <- possible_parents[names(possible_parents) %in% valid_relationships]
    }
    names(AnnotationDbi::Term(possible_parents))
  }, error = function(e) {
    c()
  })
  
  # only go down one path if desired
  if (! all_paths) {
    parents <- parents[1]
  }
  parents <- parents[parents != "all"]
  
  if (is.null(parents)) {
    return(c())
  } else if (length(parents) == 0) {
    cat(length(x))
    return(paste0(collapse = "|", AnnotationDbi::Term(x)))
  } else {
    next_x <- lapply(parents, function(y) c(y, x))
    
    # Run this function on them to get their output
    child_output <- lapply(next_x, term_class, all_paths = all_paths, type = type)
    output <- unlist(child_output, recursive = FALSE)
    
    return(output)
  }
}

cc_class <- lapply(res$go_id, term_class, all_paths = FALSE, type = GOCCPARENTS)

## 55344646434566443568574246946246485566422686555546464854425646355675264568667442845484545445734645543554335646524765144556452444453655326645667643665636437266365553666653554443547755544665656656617564665446764556665646573766647856634425556465333555454566443555466577663445647654765543454444555234434445463554664643554674427516626436466345327355425644457463455625334565764765555635665654446226564456762536437552426424656415646765565365355455756667445245644673555633443446654543448365557356745464253446355646785353564652445438566974644544762464564634664566546216445464625664546666543764666444444557654654656476665655364555668566664854733555844645856664356564664764543466574444544356564547444262664234462626633544766644566543556647578368774345366535665564555456356267443576546528764534646666576656356635466665625643444667455645576344444653635446345333645354667767555366575455346454433732665346654666546466456345545645366544686434676466536673255474444236441454645576557246444556354557336765458552565333524444754444253455444543665426466175443537538636645585636574355342552445655557566345555345644757445546655644434556167755254476552563345555354668456653657854564542555645545433556535564335646574743483668645444465355767753557465646345643455555652556456665565755459696647646565456448454854545446325563564144555435463474564654668563646663464436664536258563553454463443634557553564532442563665347464855535674557465267425626544442455633631664266666646655566654445644646592354355636446452525667273554452465531665644744355765545245442346544334444665554464753645566524645566676564644467265355623634565665426463954554635374633443685444655654624566442445442636345338566874255665749365544663266446546666434376545635875447568575244665554476457534564445646656432332565556423465455545643363653654624424644655543523554424247634445736554565644665565434525245648462424664425174667444543645645663462442426354655334565444664664345656544445442426456554333534235555445226557466427453466452355885266552564543866754823454536276665554535554328424454455474453445344555544572383854525444254153554469572265563545646326556565635354526685344274756384355575133565246443443535225577464654626645465944665455364555543266254375454664425663635469256464555532664444645645153766643556454444455213625466563353656664635452446463434537564635565452645654333346653543455445657646648341453536557355544554654362364544653566246466565334356655246424446454654264466554456555545366546144356265546659564564554664365453424456453255736464354554425234534455553474810335445455444354445764674435344375465246644535444456466236654536635334664464443454454232666452343456652645625353467445555754233555632495655145235443375654634425465473554665474555567446467444434572269427544343443444445745544442554547452353452442454654546435454522444656777254345444484254243465456684585425564554564535555545455253522445646654547865735452466566494446423445535954153345756533463454334666446344623455424455341464366457726846646264654456624635344656744675445443455355534656456354544344456532443664576614662544234526654544352447545553454555342646345656361665525656534636555546456442545444434484446554425464444625424427552442324743546245485528454425234255664255444345462456544567645347654444573556324634532434474363444747733656444824445575645755622552637232764854445543453562454242154

mf_class <- lapply(res$go_id, term_class, all_paths = FALSE, type = GOMFPARENTS)

## 27363793977553475648854835633538103143335341010369335327571076555156561966781795534556172657573545363335377356467141554594158753467383383373147233367855118743534333595343159353827515533686563334538923345108788776104655275815356236385665357365337563667537543141663638885535257798104351053529552355334664333533833133333639383357477315799556366839553657354747687534665769866815423473658576339432435976531153357376355354835663356365653373551656109414310338329163494857663399516738964761063669105911368754106563427496656455763246468732866562641965856936548375634781065753844765895743366410673348545369510736164315533101345883844866527665435664554836735455646573744338682555335754768676536537485931883355104663633342368510383666654410166538483476848495559410655478665583853353733633371071653677386363466186545667447648753665384310839104457536335333856363107377594553861584339955103638653561033745796455641053658625536183648352341344334468514666346846333885515937636569414633645546557443695464475453551063373465637633656356726584336315437106333105514535131345368328536376576439187994453333353686665214323854342558586101447337634433686833686333351146533438635537101181733849363634543344649611456575687416453761565239236596109361135576333774551634561127453667353376343332352391105353563783763774136672543621949743736633796104716633555672346755456263335103461737563579163333375365563310345337533237475444571043663355993656353337335316957545713533443333953573311694354389665695615495310106337515101036757687654366553351035463855147535531643438574349412101110314735673866546813317136567104335334679103345335458335310686647531035643657103710351454553536388610866636537388331455258723463675565463136893155365337331745326796859853467133514536638765431337534537236465383339615135565675435565136103269777104563332356593567776871075345558433381761676663443463331863343331864356456351843556151831072421558103358633333337614368467857333386533636666764566338462367686610633631051024545463310623336387575966652664633773567565367237658594187793543334635316336443891458375353335532954536101033881685653363377631085348833743464910646663635343536357498555595538453954438549591663453664555651055635736337288356414436833333356746665435665236163756971710555610569354315897486556104323533596475676345333353341373103563365778334455101458555483333935315778343113310633393751166661338103134489341037337554534376354415389575133491495643946313755564101956867577671679355365524353317556435431058334555673453377975566838736392337145591110586773385778361867251043435545646610166744613545457553335673315657535358513785555313633366443999333515109337753103834741055434336546671576354434510374965583573738434334365567675551033875614643133435753637108513565563355565658563645666368695254485163388334173363633933756346885733156565775966345358335273654310311385510613346136958633575464468843333373539739735653353543455737653868333655533153554551861056358365433535744566351553551033537366696633757673558292562253543638516945538657310517466333337895785651812645354456353633853511536663383574383433373343436359977533673335653335631035463785836277114736353733311317737385847385355833724531310284664427533483384685733366351277623338777437343162766537653563333553443359101225635373582348833387764317375381082736453335617577763736121651379333575375551336447661095775716634613531410777337351053103335544510104513351676371458326543645493764343389633136443935585434615710371156743364811087373474881599576666887537312346367355767456910146765634353763533663556746486538874466613764335691673641753442633563383311117353817366541333434531343853437576861636973865873614376513333551367377763551453683561154714573310966554338435615563315515767106510610353334455585733874962793443537737975753377368515365483955117566177656565363333916866538137923113155559988564363553953736666455675345153353533368634628433104533926374533363865669386856346111103353381052182610663374132543384468587333465753534383816945769351662753659568396746563372466616546105333753765537563164533375354556769373372593345363434513361038357331261012543535443563594638543310533336532336633363151063513367182753353153343363935335310333458567537353638453457461553465344331386738106766914733558311715335636443561034333517657363583367310735996715739613643536773335598538365765103344684644743951317433953565333542534595988874973573966684635686546102375574536656633435351536536933373105635456493526318343653577777433236671032352433553433943787319773614237693283635339343834368554343633531433388335334665814851535610966366415948110665310655551333435672333618535643105435282233657397579311010438635615384633135562537863143687333535571736333137833337233383465544553123573410737132102439736456675265131746965353535643534373555555553317351775383546658134331642557195310375566356377410743486343433634453393853521446353743435363645663934344756638333531331173364365698558117451047423634382673136556353336937103310334336647773156535361073647563335665437333751511054336693316545773536433765103133566339377776675333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bp_class <- lapply(res$go_id, term_class, all_paths = FALSE, type = GOBPPARENTS)

## 8849745556574541097587561045114979797697776105748458776966744910457975976969101011999779856710966569741012988464127667889679657956669125119117411754679896554644410655968544511912866557971098951056789477686568741195696644851049475699954968875912895897541071212911510796757107485991159397712751141079994755791298481177109755571294879696666961161196910551094576412410568109101043119465575461049499111197977599677654787498791012597986944755885651010596771077411749839697410779951051191095610446469107569810574498779986797559799557101155555885859584295989554889107751076676939687741177851079471151111659751095910987810125910128410129879511557611959598758465574748761111511887849996465577554671057891074586109745499676781112671257911111041142711115559710764710981112655810510107511108757117479891169697957799589777991195799577996910115976879554841074866871167877961147767699498579712655551045585294971291191047555971167611545442599479549119577675955749854512966546105695115724127798994958899971079107114991194111157748911510654784891010756105101195581010791165912894595410125851164106779997799949795127129965891210104744961055876645410974554979111171298577106106106771059887111175894116134711976510496959121159781259610799126109115699441056449654610971158571110861241279108641157118410947461010779412119359559969696997556997117965775896564610576669411756991159751253751045648109101165911868810751011999106754746879965101255995125886558512996910105685924799766574581074884946558469119747949107769994795571056812997910669510511663984867664610856698129499555596875111176759977659105641067573959986666961111115711510794639109659912591096996128591291010645654669279610104951091165105910106989799459115774498595878456927610781277981059971061396610447711594115105954479664685695911956912810912118711949128119859446811995487109109779985859961191061398697118969864776697676757433795967669911796929510254777551095115597674676794774498104867114597991199114479479737999861181111857448896109510951094117495551210591079967588984747547796894584911101174979858795957661197710449879610119525109511109597944812677457117769104578968875710755511789967751054984117651049576691199910841071112997917844698612101112555499791091077664115975119796104117107747484104996899971297597104774669948659965411910978911747777996659566274675479458897105798947129595791051094688497510711591079796694771448911105984981276657910727999881046546117576756107699676747861047114476864611681010557957551055491057122969611861087969958549999128958927117911996965441010979976564555124671189546581079691068911464665977691075697699947105108895911661277867101069589712599551059712744679599467956978411868912455579612710694795107498879294969107991077486965296551158897481111811510976510797858101087461111125895711710791189456119397759999454481195995678565710596118991177610894106101111969779996968810116665410995647567558451099712997115977746441099577741010977575135610855555449596678769748811541057941010498105665981274115579755991155794611499115487945547510912459763699599569499667569878645105641097991051098899941085101210109116496951051179591211510751049656129746117710510671148591110105131046479277981149778745910978844791010910654511551058710879111097594109465474475549610101176575121010610411645105997561196510869499946394955102981067512118796558991149998756685612591154656104981099699767610677749497251157991011965571176910958485109791164845444861091054649910489576511957765955764478951164559897995455876116118989659541064691196997118976547104658776951097456644910557775794106410446576910105596951112698127891211125611557710677977104776966589445557524961161067896986749995994685585955119125988796884994871111106491010936114698959789961049584116911995576661112610777119479712811777559377410765712910968499785579107111012647644491251212510511868948567119976961286511986991011694710811109991011779249856810781066994677971010994477999669811994412481281041279765546467475696975767799578999987851061081056795910761046912696579697974976355105129898589779792951159911119967118549585472469596811810976651244641177599969995510811811410587979799109699494999787968105991098696898979999866979584947876710865647991269115954794696118588859842965485778994107549896846610544895797965112499107778487979116977766976449748461297499764955551244454955957451184589687510109879912799127957101299547910575107974965921065699874956576999469997549711899899667978488774109425117751146981995448956910689681295655951098710924776844479961065796544771081151154469795869107997469841161175711119796787991011961097127109468799551051061076795457862410971199799124710811987812912597889996945591059759511810755699896109101011698105774597663865955895744446710910859486666424945661069777467954510789847892485977785795969857104510791286422396789969757971077579377711547101069791048659410610974471158561081211048695789954798849964594749105747957777928967711787910127595610

Cellular component

cc_res <- res[rep(1:nrow(res), sapply(cc_class, length)), ]
cc_res$class <- unlist(cc_class)
library(metacoder)
data <- parse_taxonomy_table(input = as.data.frame(cc_res),
                             taxon_col = c("class" = -1),
                             other_col_type = "obs_info",
                             class_sep = "\\|")
data$obs_data$padj <- as.numeric(data$obs_data$padj)
data$taxon_funcs <- c(data$taxon_funcs,
                      change = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 obs_change <- data$obs_data[i, ]$log2FoldChange[data$obs_data[i, ]$padj <= min_p_value]
                                 mean(obs_change, na.rm = TRUE)
                               },
                               numeric(1))
                      },
                      num_changed = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 sum(data$obs_data[i, ]$padj <= min_p_value, na.rm = TRUE)
                               },
                               numeric(1))
                      })

set.seed(3)
data %>%
  filter_taxa(num_changed > 0) %>%
  filter_taxa(n_supertaxa <= 4) %>%
  # filter_taxa(n_supertaxa >= 1) %>% 
  filter_taxa(nchar(name) <= 40) %>%
  heat_tree(node_label = ifelse(nchar(name) <= 40, name, NA),
            node_size = num_changed,
            # node_size_trans = "log10",
            node_size_range = c(0.01, 0.03),
            # node_label_size_trans = "log10",
            node_label_size_range = c(0.01, 0.015),
            # edge_size_trans = "log10",
            edge_size_range = c(0.008, 0.03) / 2,
            node_color = 2^abs(change) * sign(change),
            node_color_trans = "linear",
            node_color_range = diverging_palette(),
            node_color_interval = c(-4, 4),
            edge_color_trans = "linear",
            edge_color_range = diverging_palette(),
            edge_color_interval =  c(-4, 4),
            node_label_max = 500,
            node_color_axis_label = "Factor change",
            node_size_axis_label = "Number of genes",
            layout = "da", initial_layout = "re",
            output_file = result_path("gene_expression--cellular_component"))

Biological Process

bp_res <- res[rep(1:nrow(res), sapply(bp_class, length)), ]
bp_res$class <- unlist(bp_class)
library(metacoder)
data <- parse_taxonomy_table(input = as.data.frame(bp_res),
                             taxon_col = c("class" = -1),
                             other_col_type = "obs_info",
                             class_sep = "\\|")
data$obs_data$padj <- as.numeric(data$obs_data$padj)
data$taxon_funcs <- c(data$taxon_funcs,
                      change = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 obs_change <- data$obs_data[i, ]$log2FoldChange[data$obs_data[i, ]$padj <= min_p_value]
                                 mean(obs_change, na.rm = TRUE)
                               },
                               numeric(1))
                      },
                      num_changed = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 sum(data$obs_data[i, ]$padj <= min_p_value, na.rm = TRUE)
                               },
                               numeric(1))
                      })

set.seed(7) #2, 4, 5, 7*, 19
mgsub <- function(pattern, replacement, x, ...) { # from: http://stackoverflow.com/questions/15253954/replace-multiple-arguments-with-gsub
  if (length(pattern)!=length(replacement)) {
    stop("pattern and replacement do not have the same length.")
  }
  result <- x
  for (i in 1:length(pattern)) {
    result <- gsub(pattern[i], replacement[i], result, ...)
  }
  result
}

to_replace <- matrix(ncol = 2, byrow = TRUE,
                     c("regulation of growth", "",
                       "activation of innate immune response", "",
                       "system development", "",
                       "regulation of response to stimulus", "",
                       "lipid metabolic process", "",
                       "selenium compound metabolic process", "selenium metabolic process"
                       ))
output_path <- file.path(output_folder,
                                    paste0("gene_expression--biological_process",
                                           output_format))
data %>%
  filter_taxa(num_changed > 0) %>%
  filter_taxa(n_supertaxa <= 3) %>%
  mutate_taxa(name = gsub("_", " ", name),
              f_change = 2^abs(change) * sign(change)) %>%
  mutate_taxa(short_name = vapply(name, FUN.VALUE = character(1), function(x) {
    mgsub(pattern = to_replace[, 1], replacement =  to_replace[, 2], x, fixed = TRUE)
  })) %>%
  heat_tree(node_label = ifelse(abs(f_change) > 1, short_name, NA),
            node_size = num_changed,
            # node_size_trans = "log10",
            node_size_range = c(0.008, 0.03),
            # node_label_size_trans = "log10",
            node_label_size_range = c(0.012, 0.02),
            # edge_size_trans = "log10",
            edge_size_range = c(0.008, 0.03) / 2,
            node_color = f_change,
            node_color_trans = "linear",
            node_color_range = diverging_palette(),
            node_color_interval = c(-5, 5),
            edge_color_trans = "linear",
            edge_color_range = diverging_palette(),
            edge_color_interval =  c(-5, 5),
            node_label_max = 80,
            node_color_axis_label = "Fold change",
            node_size_axis_label = "Number of genes",
            layout = "da", initial_layout = "re",
            output_file = result_path("gene_expression--biological_process"))

Molecular Function

mf_res <- res[rep(1:nrow(res), sapply(mf_class, length)), ]
mf_res$class <- unlist(mf_class)
library(metacoder)
data <- parse_taxonomy_table(input = as.data.frame(mf_res),
                             taxon_col = c("class" = -1),
                             other_col_type = "obs_info",
                             class_sep = "\\|")
data$obs_data$padj <- as.numeric(data$obs_data$padj)
data$taxon_funcs <- c(data$taxon_funcs,
                      change = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 obs_change <- data$obs_data[i, ]$log2FoldChange[data$obs_data[i, ]$padj <= min_p_value]
                                 mean(obs_change, na.rm = TRUE)
                               },
                               numeric(1))
                      },
                      num_changed = function(x, subset = NULL) {
                        vapply(obs(x),
                               function(i) {
                                 sum(data$obs_data[i, ]$padj <= min_p_value, na.rm = TRUE)
                               },
                               numeric(1))
                      })

data %>%
  filter_taxa(num_changed > 0) %>%
  filter_taxa(n_supertaxa <= 3) %>%
  # filter_taxa(n_supertaxa >= 1) %>% 
  filter_taxa(nchar(name) <= 40) %>%
  heat_tree(node_label = ifelse(nchar(name) <= 40, name, NA),
            node_size = num_changed,
            # node_size_trans = "log10",
            node_size_range = c(0.01, 0.03),
            # node_label_size_trans = "log10",
            node_label_size_range = c(0.01, 0.015),
            # edge_size_trans = "log10",
            edge_size_range = c(0.008, 0.03) / 2,
            node_color = 2^abs(change) * sign(change),
            node_color_trans = "linear",
            node_color_range = diverging_palette(),
            node_color_interval = c(-4, 4),
            edge_color_trans = "linear",
            edge_color_range = diverging_palette(),
            edge_color_interval =  c(-4, 4),
            node_label_max = 500,
            node_color_axis_label = "Factor change",
            node_size_axis_label = "Number of genes",
            layout = "da", initial_layout = "re",
            output_file = result_path("gene_expression--molecular_function"))

Software and packages used

sessionInfo()

## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.2 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] stats4    parallel  grid      stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] metacoder_0.1.2            org.Hs.eg.db_3.3.0        
##  [3] GO.db_3.3.0                AnnotationDbi_1.34.4      
##  [5] DESeq2_1.12.4              airway_0.106.2            
##  [7] SummarizedExperiment_1.2.3 Biobase_2.32.0            
##  [9] GenomicRanges_1.24.3       GenomeInfoDb_1.8.7        
## [11] IRanges_2.6.1              S4Vectors_0.10.3          
## [13] BiocGenerics_0.18.0        rnaseqGene_0.99.121218    
## [15] knitcitations_1.0.7        knitr_1.14                
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-128                  bitops_1.0-6                 
##  [3] matrixStats_0.51.0            lubridate_1.6.0              
##  [5] RColorBrewer_1.1-2            httr_1.2.1                   
##  [7] rprojroot_1.2                 fission_0.106.2              
##  [9] tools_3.3.1                   backports_1.0.5              
## [11] R6_2.2.0                      rpart_4.1-10                 
## [13] Hmisc_3.17-4                  DBI_0.5-1                    
## [15] lazyeval_0.2.0                Gviz_1.16.5                  
## [17] mgcv_1.8-15                   colorspace_1.2-7             
## [19] nnet_7.3-12                   gridExtra_2.2.1              
## [21] formatR_1.4                   labeling_0.3                 
## [23] rtracklayer_1.32.2            scales_0.4.1                 
## [25] genefilter_1.54.2             stringr_1.1.0                
## [27] digest_0.6.12                 Rsamtools_1.24.0             
## [29] foreign_0.8-67                rmarkdown_1.3                
## [31] XVector_0.12.1                dichromat_2.0-0              
## [33] htmltools_0.3.5               bibtex_0.4.0                 
## [35] PoiClaClu_1.0.2               ensembldb_1.4.7              
## [37] BSgenome_1.40.1               RSQLite_1.0.0                
## [39] BiocInstaller_1.22.3          shiny_0.14.1                 
## [41] BiocParallel_1.6.6            dplyr_0.5.0                  
## [43] acepack_1.3-3.3               VariantAnnotation_1.18.7     
## [45] RCurl_1.95-4.8                magrittr_1.5                 
## [47] Formula_1.2-1                 Matrix_1.2-7.1               
## [49] Rcpp_0.12.9                   munsell_0.4.3                
## [51] RefManageR_0.13.1             stringi_1.1.2                
## [53] yaml_2.1.13                   RJSONIO_1.3-0                
## [55] zlibbioc_1.18.0               plyr_1.8.4                   
## [57] AnnotationHub_2.4.2           lattice_0.20-34              
## [59] Biostrings_2.40.2             splines_3.3.1                
## [61] GenomicFeatures_1.24.5        annotate_1.50.1              
## [63] locfit_1.5-9.1                igraph_1.0.1                 
## [65] reshape2_1.4.2                geneplotter_1.50.0           
## [67] biomaRt_2.28.0                XML_3.98-1.4                 
## [69] evaluate_0.10                 biovizBase_1.20.0            
## [71] latticeExtra_0.6-28           data.table_1.10.4            
## [73] httpuv_1.3.3                  gtable_0.2.0                 
## [75] assertthat_0.1                ggplot2_2.2.1                
## [77] mime_0.5                      xtable_1.8-2                 
## [79] survival_2.39-5               tibble_1.2                   
## [81] pheatmap_1.0.8                GenomicAlignments_1.8.4      
## [83] cluster_2.0.4                 sva_3.20.0                   
## [85] interactiveDisplayBase_1.10.3 BiocStyle_2.0.3

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